Priority Effects in the Apple Flower Determine If the Siderophore Desferrioxamine Is a Virulence Factor for Erwinia amylovora CFBP1430.

Iron is crucial for bacterial growth and virulence. Under iron-deficiency bacteria produce siderophores, iron chelators that facilitate the iron uptake into the cell via specific receptors. Erwinia amylovora, the causative agent of fire blight, produces hydroxamate-type desferrioxamine siderophores (DFO). The presented study reassesses the impact of DFO as a virulence factor of E. amylovora during its epiphytic phase on the apple flower. When inoculated in semisterile Golden Delicious flowers no difference in replication and induction of calyx necrosis could be observed between E. amylovora CFBP1430 siderophore synthesis (DfoA) or uptake (FoxR receptor) mutants and the parental strain. In addition, mutant strains only weakly induced a foxR promoter-gfpmut2 reporter construct in the flowers. When analyzing the replication of the receptor mutant in apple flowers harboring an established microbiome, either naturally, in case of orchard flowers, or by pre-inoculation of semisterile greenhouse flowers, it became evident that the mutant strain had a significantly reduced replication compared to the parental strain. The results suggest that apple flowers per se are not an iron-limiting environment for E. amylovora and that DFO is an important competition factor for the pathogen in precolonized flowers. IMPORTANCE Desferrioxamine is a siderophore produced by the fire blight pathogen E. amylovora under iron-limited conditions. In the present study, no or only weak induction of an iron-regulated promoter-GFP reporter was observed on semisterile apple flowers, and siderophore synthesis or uptake (receptor) mutants exhibited colonization of the flower and necrosis induction at parental levels. Reduced replication of the receptor mutant was observed when the flowers were precolonized by microorganisms. The results indicate that apple flowers are an iron-limited environment for E. amylovora only if precolonization with microorganisms leads to iron competition. This is an important insight for the timing of biocontrol treatments.

Changes in the Plant ß-Sitosterol/Stigmasterol Ratio Caused by the Plant Parasitic Nematode Meloidogyne incognita.

Sterols play a key role in various physiological processes of plants. Commonly, stigmasterol, ß-sitosterol and campesterol represent the main plant sterols, and cholesterol is often reported as a trace sterol. Changes in plant sterols, especially in ß-sitosterol/stigmasterol levels, can be induced by different biotic and abiotic factors. Plant parasitic nematodes, such as the root-knot nematode Meloidogyne incognita, are devastating pathogens known to circumvent plant defense mechanisms. In this study, we investigated the changes in sterols of agricultural important crops, Brassica juncea (brown mustard), Cucumis sativus (cucumber), Glycine max (soybean), Solanum lycopersicum (tomato) and Zea mays (corn), 21 days post inoculation (dpi) with M. incognita. The main changes affected the ß-sitosterol/stigmasterol ratio, with an increase of ß-sitosterol and a decrease of stigmasterol in S. lycopersicum, G. max, C. sativus and Z. mays. Furthermore, cholesterol levels increased in tomato, cucumber and corn, while cholesterol levels often were below the detection limit in the respective uninfected plants. To better understand the changes in the ß-sitosterol/stigmasterol ratio, gene expression analysis was conducted in tomato cv. Moneymaker for the sterol 22C-desaturase gene CYP710A11, responsible for the conversion of ß-sitosterol to stigmasterol. Our results showed that the expression of CYP710A11 was in line with the sterol profile of tomato after M. incognita infection. Since sterols play a key role in plant-pathogen interactions, this finding opens novel insights in plant nematode interactions.

A network-based feature selection approach to identify metabolic signatures in disease.

The identification and interpretation of metabolic biomarkers is a challenging task. In this context, network-based approaches have become increasingly a key technology in systems biology allowing to capture complex interactions in biological systems. In this work, we introduce a novel network-based method to identify highly predictive biomarker candidates for disease. First, we infer two different types of networks: (i) correlation networks, and (ii) a new type of network called ratio networks. Based on these networks, we introduce scores to prioritize features using topological descriptors of the vertices. To evaluate our method we use an example dataset where quantitative targeted MS/MS analysis was applied to a total of 52 blood samples from 22 persons with obesity (BMI >30) and 30 healthy controls. Using our network-based feature selection approach we identified highly discriminating metabolites for obesity (F-score >0.85, accuracy >85%), some of which could be verified by the literature.